Framework features enabling faster establishment and better management of privately protected areas in New South Wales, Australia
Bibliographic record
Abstract
In response to the Convention on Biological Diversity’s Kunming-Montreal Global Biodiversity Framework, Australia has committed to protecting 30 per cent of lands and oceans for nature conservation by 2030. Privately protected areas are vital to meeting this target and establishing an ecologically representative and well-connected National Reserve System on land in Australia. As a federated nation, most public and privately protected areas (especially conservation covenants) are established under state or territory (i.e. subnational) legislation, as opposed to national legislation. This paper conducts a review of changes in policy and practice for private land conservation in the state of New South Wales (NSW) that has led to a marked acceleration in the establishment of privately protected areas since 2017. The historical average rate at which privately protected areas were being established in NSW under various schemes prior to the changes in 2017 was about 50 agreements and 12,000 hectares per annum. The new Biodiversity Conservation Act 2016, the Biodiversity Conservation Trust of NSW (BCT), and increased NSW Government funding commenced in August 2017. Since then, the rate of establishment of privately protected areas has accelerated to more than 100 agreements and 45,000 hectares per annum. Not only has the rate of establishment more than tripled (by area) but many more privately protected areas are being established in higher priority bioregions, and the BCT is now able to provide better financial and technical support to privately protected areas, leading to better conservation outcomes overall. Key changes that have strengthened the framework for establishing and managing privately protected areas in NSW include a guide for strategic investment; institutional arrangements that foster effective governance, trust and transparency; substantive NSW Government funding; an accumulating endowment fund model; in-perpetuity payments; and faster and more targeted delivery mechanisms. The paper highlights features that could be adopted in other jurisdictions in Australia to support the vital role that privately protected areas must play in achieving commitments to nature conservation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".